• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.416-422
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• 2019

This paper proposes a GPU-based modeling and rendering of 3D clouds using procedural functions. The formation of clouds is based on modified noise function made with fbm(Fractional Brownian Motion). Those noise values turn into densities of droplets of liquid water, which is a critical parameter for forming the three different types of clouds. At the rendering stage, the algorithm applies the ray marching technique to decide the colors of cloud using density values obtained from the noise function. In this process, all lighting attenuation and scattering are calculated by physically based manner. Once we have the clouds, they are blended on the sky, which is also rendered physically. We also make the clouds moving in the sky by the wind force. All algorithms are implemented and tested on GPU using GLSL.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.72-77
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• 2014

There are two main technologies to implement cloud effect in flight simulator, cloud modeling using particle system and texture mapping. In former case, this approach may cause a low frame rate while unrealistic cloud effect is observed in latter case. To Solve this problem, in this paper, we propose how to apply fog effect into camera to display more realistic cloud effect with high frame rate. The proposed method is tested with massive terrain database environment through implemented software by using OpenSceneGraph. As a result, compared to texture mapping method, the degree of difference on frame rate is 1 or 2Hz while the cloud effect is significantly improved as realistic as particle system.

• The KIPS Transactions:PartA
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• v.16A no.5
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• pp.307-318
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• 2009

Modeling and rendering of atmospheric phenomena such as clouds is one of most difficult research themes in the field of computer graphics, mainly due to its complexity, huge volume, ubiquitousness, etc. In this paper, we represent a system for real-time modeling and rendering of clouds, mainly aiming at the computer games and flight simulation applications. Our implementation generates various kinds of clouds including cirrus, stratus, and cumulus, through intuitive real-timeuser interactions. Then, additional details are automatically attached to them, using our own methods based on meta-balls or hierarchical spherical particles. After processing multiple scattering and anisotropic scattering, resulting particles are rendered into billboards, to finally achieve real-time processing.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.147-156
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• 2010

In this paper, we propose 3D cloud animation by cloud modeling method of 2D images retrieved from a meteorological satellite. First, on the satellite images, we locate numerous control points to perform thin-plate spline warping analysis between consecutive frames for the modeling of cloud motion. In addition, the spectrum channels of visible and infrared wavelengths are used to determine the amount and altitude of clouds for 3D cloud image reconstruction. Pre-integrated volume rendering method is used to achieve seamless inter-laminar shades in real-time using small number of slices of the volume data. The proposed method could successfully construct continuously moving 3D clouds from 2D satellite images at an acceptable speed and image quality.

• Atmosphere
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• v.33 no.2
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• pp.105-124
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• 2023

Research papers published in the Korean Meteorological Society (KMS) journals by the members of KMS since the establishment of KMS in 1963 in the field of atmospheric physics are summarized. A significant number of research papers published in other international journals are also cited in this paper to highlight the achievement of the KMS members in international academic community. The aim is to illustrate the historical development of research activities of the KMS members in the field of atmospheric physics, and indeed it is found that the KMS members have made enormous progress in research publications quantitatively and qualitatively in the field of atmospheric physics. In detail, however, observational studies of aerosol physical properties and cloud and precipitation physics were very active, and studies on cloud physics parameterization for cloud modeling were highly recognized in the world, but observational and theoretical studies of atmospheric radiation were relatively lacking and solicit more contribution from the KMS members.

• Journal of the Korean Society for Railway
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• v.11 no.3
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• pp.311-316
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• 2008

Ratcheting is a cyclic accumulation of strain under a cyclic loading. It is a kind of mechanisms which generate cracks in rail steels. Though some experimental and numerical study has been performed, modeling of ratcheting is still a challenging problem. In this study, an elastic-plastic constitutive equation with non-linear kinematic hardening equation was applied. Contact stresses in wheel-rail were analyzed. Under the tangential stress of the contact stresses, a cyclic stress-strain relation was obtained by using the model. A constant ratcheting strain per cycle was accumulated.

• Korean Journal of Remote Sensing
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• v.15 no.1
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• pp.21-30
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• 1999

The CLW (Cloud Liquid Water) is a parameter of vital interest in both modeling and forecasting weather. In mesoscale models, the magnitude of latent heat effects corresponds to the amount of CLW, which is important in the development of a certain weather system. The goal of this study is the estimation of CLW by GMS-5 data which is compared with that of SSM/I data and GMR(Grounded Microwave Radiometer)data. First of all, we found out the relationship of cloud albedo to cloud thickness, and caculated the CLW using the result of the relationship. The CLW amount of SSM/I or GMR and that of GMS-5 were compared, respectively. The correlation coefficient was about 0.86 and RMSE was 9.23 mg/$cm^2$ between GMS-5 data and GMR data. And also the correlation coefficient was 0.84 and RMSE was 14.02 mg/$cm^2$ between GMS-5 data and SSM/I data.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.1-8
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• 2023

Modeling of artificial targets in Synthetic Aperture radar (SAR) mainly simulates radar signals reflected from the faces and edges of the 3D Computer Aided Design (CAD) model with a ray-tracing method, and modeling of the clutter on the Earth's surface uses a method of distinguishing types with similar distribution characteristics through statistical analysis of the SAR image itself. In this paper, man-made targets on the surface and background clutter on the terrain are integrated and made into a three-dimensional (3D) point cloud scatterer model, and SAR image were created through computational signal processing. The results of the SAR Stripmap image generation of the actual automobile based SAR radar system and the results analyzed using EM modeling or statistical distribution models are compared with this 3D point cloud scatterer model. The modeling target is selected as an bridge because it has the characteristic of having both water surface and ground terrain around the bridge and is also a target of great interest in both military and civilian use.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.